Power of Derived Tables in Looker: A Game Changer for Data Analysis

Looker is a powerful business intelligence tool that enables organizations to explore and analyze their data in real-time. One of the most impactful features of Looker is its derived tables, which can greatly simplify the data analysis process. In this blog post, we will explore what derived tables are, why they are important, and how they can be used to streamline your data analysis process.

What Are Derived Tables in Looker?

Derived tables in Looker are essentially virtual tables that are created by combining, transforming, and aggregating data from existing tables. These virtual tables can be used in place of the original data source to create reports, dashboards, and charts, without having to modify the original data.

types of derived tables – click here

Why Use Derived Tables in Looker?

Derived tables provide several benefits, including:

1. Improved performance: By creating a virtual table, you can reduce the amount of data that needs to be loaded into memory, resulting in faster report execution times.
2. Simplified data analysis: Derived tables allow you to create a single source of truth for your data, making it easier to analyze and explore your data.
3. Increased collaboration: Derived tables can be shared with others, allowing team members to access and use them, which can help increase collaboration and improve data accuracy.

How to Use Derived Tables in Looker

Using derived tables in Looker is easy and can be done in a few simple steps:

1. Create a new derived table: You can create a new derived table by clicking on the “Create” button in the Looker interface and selecting “Derived Table”.
2. Define your derived table: Once you have created a new derived table, you will need to define the columns, conditions, and aggregations that will be used to create the virtual table.
3. Use your derived table: Once your derived table is created, you can use it in place of the original data source to create reports, dashboards, and charts.

Use Case: Analyzing Website Traffic Data

Let’s say you want to analyze your website traffic data to understand the number of visitors, their location, and the pages they visit. To do this, you would typically need to extract the data from your website analytics tool, import it into Looker, and then analyze it.

With derived tables in Looker, you can simplify this process by creating a derived table that aggregates the data from your website analytics tool. This virtual table can be used in place of the original data source to create reports and dashboards, without having to import the data into Looker.

here are some additional examples of how derived tables can be used in Looker:

1. Sales Analysis: If you want to analyze your sales data, you can create a derived table that aggregates the data by product, region, and quarter. This virtual table can then be used to create reports and dashboards that show sales trends over time.
2. Customer Segmentation: If you want to segment your customers based on their behavior, you can create a derived table that aggregates the data by customer, purchase history, and demographics. This virtual table can then be used to create reports and dashboards that show how different customer segments behave.
3. Inventory Management: If you want to analyze your inventory data, you can create a derived table that aggregates the data by product, supplier, and stock level. This virtual table can then be used to create reports and dashboards that show inventory trends over time.
4. Human Resources: If you want to analyze your HR data, you can create a derived table that aggregates the data by employee, job role, and department. This virtual table can then be used to create reports and dashboards that show HR trends over time.
5. Marketing Analytics: If you want to analyze your marketing data, you can create a derived table that aggregates the data by campaign, channel, and target audience. This virtual table can then be used to create reports and dashboards that show marketing trends over time.

These are just a few examples of how derived tables can be used in Looker. With the ability to aggregate, transform, and combine data from multiple sources, the possibilities are virtually limitless.

In conclusion, derived tables in Looker are a game changer for data analysis. By simplifying the data analysis process and reducing the amount of data that needs to be loaded into memory, they can help organizations make more informed decisions, faster. If you are using Looker and have not yet explored the power of derived tables, we encourage you to give them a try and see the results for yourself.

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About Me:-
I am Om Prakash Singh – Data Analytics Consultant , Looker Consultant , Solution Architect .
I am Highly analytical and process-oriented Data Analyst with in-depth knowledge of database types; research methodologies; and big data capture, manipulation and visualization. Furnish insights, analytics and business intelligence used to advance opportunity identification.

You’ve got data and lots of it. If you’re like most enterprises, you’re struggling to transform massive information into actionable insights for better decision-making and increased business results.
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